docs: def get_mappings_for_fit(self, dense=False):
          """
          Parameters
          ----------
          dense : bool, optional.
              Dictates if sparse matrices will be returned or dense numpy arrays.
  
          Returns
          -------
          mapping_dict : OrderedDict.
              Keys will be `["rows_to_obs", "rows_to_alts", "chosen_row_to_obs",
              "rows_to_nests"]`. The value for `rows_to_obs` will map the rows of
              the `long_form` to the unique observations (on the columns) in
              their order of appearance. The value for `rows_to_alts` will map
              the rows of the `long_form` to the unique alternatives which are
              possible in the dataset (on the columns), in sorted order--not
              order of appearance. The value for `chosen_row_to_obs`, if not
              None, will map the rows of the `long_form` that contain the chosen
              alternatives to the specific observations those rows are associated
              with (denoted by the columns). The value of `rows_to_nests`, if not
              None, will map the rows of the `long_form` to the nest (denoted by
              the column) that contains the row's alternative. If `dense==True`,
              the returned values will be dense numpy arrays. Otherwise, the
              returned values will be scipy sparse arrays.
          """
          return create_long_form_mappings(self.data,
                                           self.obs_id_col,
                                           self.alt_id_col,
                                           choice_col=self.choice_col,
                                           nest_spec=self.nest_spec,
                                           mix_id_col=self.mixing_id_col,
                                           dense=dense)